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10 - Biobanking and Biomarkers in the Alzheimer’s Disease Drug-Development Ecosystem

from Section 2 - Non-clinical Assessment of Alzheimer’s Disease Candidate Drugs

Published online by Cambridge University Press:  03 March 2022

Jeffrey Cummings
Affiliation:
University of Nevada, Las Vegas
Jefferson Kinney
Affiliation:
University of Nevada, Las Vegas
Howard Fillit
Affiliation:
Alzheimer’s Drug Discovery Foundation
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Summary

Biobanks and biomarker discovery workflows have grown to be an essential piece of clinical trial research to advance candidate therapeutics. While initially biobanks were established for safety and tolerability examinations they now provide data on inclusion measures for clinical trials, as well as numerous outcome measures that inform on target engagement to disease-modifying effects. This process is complex as there is tremendous need for standardization of everything from sample collection and storage to reproducibility of experimental data. With biomarker discovery capabilities advancing at a rapid pace novel techniques in various samples have also become part of the biobank workflow. In this chapter we highlight some of the prevailing considerations in biobanking and biomarker discovery. We also highlight the approaches that are emerging as the next steps in biomarker discovery in Alzheimer’s disease clinical trial research.

Type
Chapter
Information
Alzheimer's Disease Drug Development
Research and Development Ecosystem
, pp. 123 - 134
Publisher: Cambridge University Press
Print publication year: 2022

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References

Hallmans, G, Vaught, JB. Best practices for establishing a biobank. In Methods in Biobanking, Dillner, J (ed.). New York: Humana Press; 2011: 241–60.Google Scholar
Betsou, F, Lehmann, S, Ashton, G, et al. Standard preanalytical coding for biospecimens: defining the sample preanalytical code. Cancer Epidemiol Prev Biomark 2010; 19: 1004–11.Google Scholar
Dillner, J (ed.). Methods in Biobanking. New York: Humana Press; 2011.CrossRefGoogle Scholar
Lehmann, S, Guadagni, F, Mooreet, H, et al. Standard preanalytical coding for biospecimens: review and implementation of the Sample PREanalytical Code (SPREC). Biopreserv Biobank 2012; 10: 366–74.CrossRefGoogle ScholarPubMed
McQueen, MJ, Keys, JL, Bamford, K, Hall, K. The challenge of establishing, growing and sustaining a large biobank: a personal perspective. Clin Biochem 2014; 47: 239–44.Google Scholar
Cummings, J, Lee, G, Ritter, A, Sabbagh, M, Zhong, K. Alzheimer’s disease drug development pipeline: 2020. Alzheimers Dement (N Y) 2020; 6: e12050.CrossRefGoogle ScholarPubMed
Cummings, J. The role of biomarkers in Alzheimer’s disease drug development. In Reviews on Biomarker Studies in Psychiatric and Neurodegenerative Disorders, Guest, PC (ed.). New York: Springer; 2019: 2961.Google Scholar
Jack, CR, Vemuri, P, Wistet, HJ, et al. Evidence for ordering of Alzheimer’s disease biomarkers. Arch Neurol 2011; 68: 1526–35.CrossRefGoogle Scholar
Ashton, NJ, Leuzy, A, Lim, YM, et al. Increased plasma neurofilament light chain concentration correlates with severity of post-mortem neurofibrillary tangle pathology and neurodegeneration. Acta Neuropathol Commun 2019; 7: 5.Google Scholar
Mattsson, N, Andreasson, U, Zetterberg, H, Blennow, K. Association of plasma neurofilament light with neurodegeneration in patients with Alzheimer disease. JAMA Neurol 2017; 74: 557–66.CrossRefGoogle ScholarPubMed
Mattsson, N, Cullen, NC, Andreasson, U, Zetterberg, H, Blennow, K. Association between longitudinal plasma neurofilament light and neurodegeneration in patients with Alzheimer disease. JAMA Neurol 2019; 76: 791–9.CrossRefGoogle ScholarPubMed
Pereira, JB, Westman, E, Hansson, O. Association between cerebrospinal fluid and plasma neurodegeneration biomarkers with brain atrophy in Alzheimer’s disease. Neurobiol Aging 2017; 58: 1429.CrossRefGoogle ScholarPubMed
Preische, O, Schultz, SA, Apel, A, et al. Serum neurofilament dynamics predicts neurodegeneration and clinical progression in presymptomatic Alzheimer’s disease. Nat Med 2019; 25: 277–83.Google Scholar
Rojas, JC, Karydas, A, Bang, J, et al. Plasma neurofilament light chain predicts progression in progressive supranuclear palsy. Ann Clin Transl Neurol 2016; 3: 216–25.CrossRefGoogle ScholarPubMed
Sánchez-Valle, R, Heslegrave, A, Foiani, MS, et al. Serum neurofilament light levels correlate with severity measures and neurodegeneration markers in autosomal dominant Alzheimer’s disease. Alzheimers Res Ther 2018; 10: 113.Google Scholar
Janelidze, S, Hertze, J, Zetterberg, H, et al. Cerebrospinal fluid neurogranin and YKL-40 as biomarkers of Alzheimer’s disease. Ann Clin Transl Neurol 2015; 3: 1220.CrossRefGoogle ScholarPubMed
Liu, W, Lin, H, He, X, et al. Neurogranin as a cognitive biomarker in cerebrospinal fluid and blood exosomes for Alzheimer’s disease and mild cognitive impairment. Transl Psychiatry 2020; 10: 19.Google Scholar
Portelius, E, Olsson, B, Hoglund, K, et al. Cerebrospinal fluid neurogranin concentration in neurodegeneration: relation to clinical phenotypes and neuropathology. Acta Neuropathol 2018; 136: 363–76.CrossRefGoogle ScholarPubMed
Wellington, H, Paterson, RW, Portelius, E, et al. Increased CSF neurogranin concentration is specific to Alzheimer disease. Neurology 2016; 86: 829–35.CrossRefGoogle ScholarPubMed
Liu, G, Sun, J-Y, Xu, M, Yang, X-Y, Sun, B-L. SORL1 variants show different association with early-onset and late-onset Alzheimer’s disease risk. J Alzheimers Dis 2017; 58: 1121–8.Google Scholar
Nicolas, G, Charbonnier, C, Wallon, D, et al. SORL1 rare variants: a major risk factor for familial early-onset Alzheimer’s disease. Mol Psychiatry 2016; 21: 831–6.CrossRefGoogle Scholar
Pottier, C, Hannequin, D, Coutant, S, et al. High frequency of potentially pathogenic SORL1 mutations in autosomal dominant early-onset Alzheimer disease. Mol Psychiatry 2012; 17: 875–9.CrossRefGoogle ScholarPubMed
Rogaeva, E, Meng, Y, Lee, JH, et al. The neuronal sortilin-related receptor SORL1 is genetically associated with Alzheimer’s disease. Nat Genet 2007; 39: 168–77.CrossRefGoogle Scholar
Thonberg, H, Chiang, H-H, Lilius, L, Forsell, C. Identification and description of three families with familial Alzheimer disease that segregate variants in the SORL1 gene. Acta Neuropathol Commun 2017; 5: 43.CrossRefGoogle ScholarPubMed
Verheijen, J, Van den Bossche, T, van der Zee, J, et al. A comprehensive study of the genetic impact of rare variants in SORL1 in European early-onset Alzheimer’s disease. Acta Neuropathol 2016; 132: 213–24.Google Scholar
Wen, Y, Miyashita, A, Kitamura, N, et al. SORL1 is genetically associated with neuropathologically characterized late-onset Alzheimer’s disease. J Alzheimers Dis 2013; 35: 387–94.Google Scholar
Kester, MI, Teunissen, CE, Sutphen, C, et al. Cerebrospinal fluid VILIP-1 and YKL-40, candidate biomarkers to diagnose, predict and monitor Alzheimer’s disease in a memory clinic cohort. Alzheimers Res Ther 2015; 7: 59.Google Scholar
Lee, J, Blennow, K, Andreasen, N, et al. The brain injury biomarker VLP-1 is increased in the cerebrospinal fluid of Alzheimer disease patients. Clin Chem 2008; 54: 1617–23.CrossRefGoogle ScholarPubMed
Tarawneh, R, D’Angelo, G, Macy, E, et al. Visinin-like protein-1: diagnostic and prognostic biomarker in Alzheimer disease. Ann Neurol 2011; 70: 274–85.Google Scholar
Kinney, JW, Bemiller, SM, Murtishaw, AS, et al. Inflammation as a central mechanism in Alzheimer’s disease. Alzheimers Dement Transl Res Clin Interv 2018; 4: 575–90.Google ScholarPubMed
Moatamed, NA. Biobanking of urine samples. In Biobanking: Methods and Protocols, Yong, WH (ed.). New York: Springer; 2019: 115–24.Google Scholar
Peña-Bautista, C, Vigor, C, Galano, J-M, et al. New screening approach for Alzheimer’s disease risk assessment from urine lipid peroxidation compounds. Sci Rep 2019; 9: 14244.CrossRefGoogle ScholarPubMed
Yao, F, Hong, X, Li, S, et al. Urine-based biomarkers for Alzheimer’s disease identified through coupling computational and experimental methods. J Alzheimers Dis 2018; 65: 421–31.CrossRefGoogle ScholarPubMed
Boston, PF, Gopalkaje, K, Manning, L, Middleton, L, Loxley, M. Developing a simple laboratory test for Alzheimer’s disease: measuring acetylcholinesterase in saliva: a pilot study. Int J Geriatr Psychiatry 2008; 23: 439–40.Google Scholar
Sayer, R, Law, E, Connelly, PJ, Breen, KC. Association of a salivary acetylcholinesterase with Alzheimer’s disease and response to cholinesterase inhibitors. Clin Biochem 2004; 37: 98104.CrossRefGoogle ScholarPubMed
Bermejo-Pareja, F, Antequera, D, Vargas, T, Molina, JA, Carro, E. Saliva levels of Abeta1–42 as potential biomarker of Alzheimer’s disease: a pilot study. BMC Neurol 2010; 10: 108.CrossRefGoogle ScholarPubMed
Lee, M, Guo, JP, Kennedy, K, McGeer, EG, McGeer, PL. A method for diagnosing Alzheimer’s disease based on salivary amyloid-β protein 42 levels. J Alzheimers Dis 2017; 55: 1175–82.Google Scholar
Tsuruoka, M, Hara, J, Hirayama, A, et al. Capillary electrophoresis-mass spectrometry-based metabolome analysis of serum and saliva from neurodegenerative dementia patients. Electrophoresis 2013; 34: 2865–72.Google Scholar
Shi, M, Sui, Y-T, Peskind, ER, et al. Salivary tau species are potential biomarkers of Alzheimer disease. J. Alzheimers Dis 2011; 27: 299305.CrossRefGoogle Scholar
Carro, E, Bartolomé, F, Bermejo-Pareja, F, et al. Early diagnosis of mild cognitive impairment and Alzheimer’s disease based on salivary lactoferrin. Alzheimers Dement Diagn Assess Dis Monit 2017; 8: 131–8.Google ScholarPubMed
Takahashi, K, Yamanaka, S. Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell 2006; 126: 663–76.Google Scholar
Ooi, L, Sidhu, K, Poljak, A, et al. Induced pluripotent stem cells as tools for disease modelling and drug discovery in Alzheimer’s disease. J. Neural Transm (Vienna) 2013; 120: 103–11.Google ScholarPubMed
Takahashi, K, Tanabe, K, Ohnuki, M, et al. Induction of pluripotent stem cells from adult human fibroblasts by defined factors. Cell 2007; 131: 861–72.Google Scholar
Yu, J, Vodyanik, MA, Smuga-Otto, K, et al. Induced pluripotent stem cell lines derived from human somatic cells. Science 2007; 318: 1917–20.Google Scholar
Yahata, N, Asai, M, Kitaoka, S, et al. Anti-Aβ drug screening platform using human iPS cell-derived neurons for the treatment of Alzheimer’s disease. PLoS One 2011; 6: e25788.Google Scholar
Yagi, T, Ito, D, Okada, Y, et al. Modeling familial Alzheimer’s disease with induced pluripotent stem cells. Hum Mol Genet 2011; 20: 4530–9.Google Scholar
Hossini, AM, Megges, M, Prigione, A, et al. Induced pluripotent stem cell-derived neuronal cells from a sporadic Alzheimer’s disease donor as a model for investigating AD-associated gene regulatory networks. BMC Genomics 2015; 16: 84.Google Scholar
Majolo, F, Marinowic, DR, Machado, DC, Da Costa, JC. Important advances in Alzheimer’s disease from the use of induced pluripotent stem cells. J Biomed Sci 2019; 26: 15.Google Scholar
Yang, J, Li, S, He, X-B, Cheng, C, Le, W. Induced pluripotent stem cells in Alzheimer’s disease: applications for disease modeling and cell-replacement therapy. Mol Neurodegener 2016; 11: 39Google Scholar
Zhang, R, Zhang, L, Xie, X. iPSCs and small molecules: a reciprocal effort towards better approaches for drug discovery. Acta Pharmacol Sin 2013; 34: 765–76.Google Scholar
Dragunow, M. The adult human brain in preclinical drug development. Nat Rev Drug Discov 2008; 7: 659–66.Google Scholar
Israel, MA, Yuan, SH, Bardy, C, et al. Probing sporadic and familial Alzheimer’s disease using induced pluripotent stem cells. Nature 2012; 482: 216–20.CrossRefGoogle ScholarPubMed
Kondo, T, Asai, M, Tsukita, K, et al. Modeling Alzheimer’s disease with iPSCs reveals stress phenotypes associated with intracellular Aβ and differential drug responsiveness. Cell Stem Cell 2013; 12: 487–96.Google Scholar
Cryan, JF, O’Riordan, KJ, Cowan, CSM, et al. The microbiota-gut–brain axis. Physiol Rev 2019; 99: 18772013.Google Scholar
Gareau, MG. Microbiota–gut–brain axis and cognitive function. In Microbial Endocrinology: The Microbiota–Gut–Brain Axis in Health and Disease, Lyte, M, Cryan, JF (eds.). New York: Springer; 2014: 357–71.Google Scholar
Quigley, EMM. Microbiota–brain–gut axis and neurodegenerative diseases. Curr Neurol Neurosci Rep 2017; 17: 94.CrossRefGoogle ScholarPubMed
Calsolaro, V, Edison, P. Neuroinflammation in Alzheimer’s disease: current evidence and future directions. Alzheimers Dement 2016; 12: 719–32.Google Scholar
Jia, W, Rajani, C, Kaddurah-Daouk, R, Li, H. Expert insights: the potential role of the gut microbiome–bile acid–brain axis in the development and progression of Alzheimer’s disease and hepatic encephalopathy. Med Res Rev 2020; 40: 1496–507.Google Scholar
Angelucci, F, Cechova, K, Amlerov, J, Hort, J. Antibiotics, gut microbiota, and Alzheimer’s disease. J Neuroinflamm 2019; 16: 108.Google Scholar
Liu, S, Gao, J, Zhu, M, Liu, K, Zhang, H-L. Gut microbiota and dysbiosis in Alzheimer’s disease: implications for pathogenesis and treatment. Mol Neurobiol 2020; 57: 5026–43.Google Scholar
He, Y, Li, B, Sun, D, Chen, S. Gut microbiota: implications in Alzheimer’s disease. J Clin Med 2020; 9: 2042.Google Scholar
Seo, D-O, Holtzman, DM. Gut microbiota: from the forgotten organ to a potential key player in the pathology of Alzheimer’s disease. J Gerontol Ser A 2020; 75: 1232–41.Google Scholar
Syed, YY. Sodium oligomannate: first approval. Drugs 2020; 80: 441–4.Google Scholar
Wang, X, Sun, G, Geng, M, et al. Sodium oligomannate therapeutically remodels gut microbiota and suppresses gut bacterial amino acids: shaped neuroinflammation to inhibit Alzheimer’s disease progression. Cell Res 2019; 29: 787803.CrossRefGoogle ScholarPubMed
Gilman, S, Koller, M, Black, RS, et al. Clinical effects of Abeta immunization (AN1792) in patients with AD in an interrupted trial. Neurology 2005; 64: 1553–62.Google Scholar
Nicoll, JAR, Buckland, GR, Harrison, CH, et al. Persistent neuropathological effects 14 years following amyloid-β immunization in Alzheimer’s disease. Brain J Neurol 2019; 142: 2113–26.CrossRefGoogle ScholarPubMed
Castle, MJ, Baltanás, FC, Kovacs, I, et al. Postmortem analysis in a clinical trial of AAV2-NGF gene therapy for Alzheimer’s disease identifies a need for improved vector delivery. Hum Gene Ther 2020; 31: 415–22.Google Scholar
Halliday, GM, Shepherd, CE, McCann, H, et al. Effect of anti-inflammatory medications on neuropathological findings in Alzheimer disease. Arch Neurol 2000; 57: 831–6.CrossRefGoogle ScholarPubMed
Beeri, MS, Schmeidler, J, Lesser, GT, et al. Corticosteroids, but not NSAIDs, are associated with less Alzheimer neuropathology. Neurobiol Aging 2012; 33: 1258–64.Google Scholar
Sparks, DL, Sabbagh, M, Connor, D, et al. Statin therapy in Alzheimer’s disease. Acta Neurol Scand Suppl 2006; 185: 7886.Google Scholar
Li, G, Larson, EB, Sonnen, JA, et al. Statin therapy is associated with reduced neuropathologic changes of Alzheimer disease. Neurology 2007; 69: 878–85.CrossRefGoogle ScholarPubMed
Cummings, JL, Ringman, J, Vinters, HV. Neuropathologic correlates of trial-related instruments for Alzheimer’s disease. Am J Neurodegener Dis 2014; 3: 45–9.Google Scholar
Clark, CM, Pontecorvo, MJ, Beach, TG, et al. Cerebral PET with florbetapir compared with neuropathology at autopsy for detection of neuritic amyloid-β plaques: a prospective cohort study. Lancet Neurol 2012; 11: 669–78.Google Scholar
Doré, V, Bullich, S, Rowe, CC, et al. Comparison of 18F-florbetaben quantification results using the standard Centiloid, MR-based, and MR-less CapAIBL® approaches: validation against histopathology. Alzheimers Dement 2019; 15: 807–16.Google Scholar
Thal, DR, Beach, TG, Zanette, M, et al. Estimation of amyloid distribution by [18F]flutemetamol PET predicts the neuropathological phase of amyloid β-protein deposition. Acta Neuropathol 2018; 136: 557–67.CrossRefGoogle ScholarPubMed
Fleisher, AS, Pontecorvo, MJ, Devous, MD, Sr., et al. Positron emission tomography imaging with [18F]flortaucipir and postmortem assessment of Alzheimer disease neuropathologic changes. JAMA Neurol 2020; 77: 829–39.Google Scholar
Apostolova, LG, Zarow, C, Biado, K, et al. Relationship between hippocampal atrophy and neuropathology markers: a 7 T MRI validation study of the EADC-ADNI Harmonized Hippocampal Segmentation Protocol. Alzheimers Dement 2015; 11: 139–50.Google Scholar
Strozyk, D, Blennow, K, White, L, Launer, LJ. CSF Aβ 42 levels correlate with amyloid-neuropathology in a population-based autopsy study. Neurology 2003; 60: 652–6.Google Scholar
Seeburger, JL, Holder, DJ, Combrinck, M, et al. Cerebrospinal fluid biomarkers distinguish postmortem-confirmed Alzheimer’s disease from other dementias and healthy controls in the OPTIMA cohort. J Alzheimers Dis 2015; 44: 525–39.Google Scholar
Horgan, RP, Kenny, LC. ‘Omic’ technologies: genomics, transcriptomics, proteomics and metabolomics. Obstet Gynaecol 2011; 13: 189–95.CrossRefGoogle Scholar
Wenk, MR. The emerging field of lipidomics. Nat Rev Drug Discov 2005; 4: 594.Google Scholar
Astarita, G, Piomelli, D. Towards a whole-body systems [multi-organ] lipidomics in Alzheimer’s disease. Prostaglandins Leukot Essent Fatty Acids 2011; 85: 197203.Google Scholar
Gatz, M, Pedersen, NL, Berg, S, et al. Heritability for Alzheimer’s disease: the study of dementia in Swedish twins. J Gerontol Ser A 1997; 52: M117–25.Google Scholar
Gatz, M, Reynolds, CA, Fratiglioni, L, et al. Role of genes and environments for explaining Alzheimer disease. Arch Gen Psychiatry 2006; 63: 168.Google Scholar
Andrews, SJ, Fulton-Howard, B, Goate, A. Interpretation of risk loci from genome-wide association studies of Alzheimer’s disease. Lancet Neurol 2020; 19: 326–35.Google Scholar
Coon, KD, Myers, AJ, Craig, DW, et al. A high-density whole-genome association study reveals that APoE is the major susceptibility gene for sporadic late-onset Alzheimer’s disease. J Clin Psychiatry 2007; 68: 613–18.Google Scholar
Grupe, A, Abraham, R, Li, Y, et al. Evidence for novel susceptibility genes for late-onset Alzheimer’s disease from a genome-wide association study of putative functional variants. Hum Mol Genet 2007; 16: 865–73.Google Scholar
Raghavan, N, Tosto, G. Genetics of Alzheimer’s disease: the importance of polygenic and epistatic components. Curr Neurol Neurosci Rep 2018; 17: 78Google Scholar
Guerreiro, R, Wojtas, A, Bras, J, et al. TREM2 variants in Alzheimer’s disease. N Engl J Med 2013; 368: 117–27.Google Scholar
Jonsson, T, Stefansson, H, Steinberg, S, et al. Variant of TREM2 associated with the risk of Alzheimer’s disease. N Engl J Med 2013; 368: 107–16.Google Scholar
Courtney, E, Kornfeld, S, Janitz, K, Janitz, M. Transcriptome profiling in neurodegenerative disease. J Neurosci Methods 2010; 193: 189202.Google Scholar
Costa, V, Angelini, C, De Feis, I, Ciccodicola, A. Uncovering the complexity of transcriptomes with RNA-seq. J Biomed Biotechnol 2010; DOI: http://doi.org/10.1155/2010/853916.Google Scholar
Burgos, K, Malenica, I, Metpally, R, et al. Profiles of extracellular miRNA in cerebrospinal fluid and serum from patients with Alzheimer’s and Parkinson’s diseases correlate with disease status and features of pathology. PLoS One 2014; 9: e94839.Google Scholar
Magistri, M, Velmeshev, D, Makhmutova, M, Faghihi, MA. Transcriptomics profiling of Alzheimer’s disease reveal neurovascular defects, altered amyloid-β homeostasis, and deregulated expression of long noncoding RNAs. J Alzheimers Dis 2015; 48: 647–65.Google Scholar
Mills, JD, Nalpathamkalam, T, Jacobs, HIL, et al. RNA-seq analysis of the parietal cortex in Alzheimer’s disease reveals alternatively spliced isoforms related to lipid metabolism. Neurosci Lett 2013; 536: 90–5.Google Scholar
Mills, JD, Janitz, M. Alternative splicing of mRNA in the molecular pathology of neurodegenerative diseases. Neurobiol Aging 2012; 33: 1012.e1124.Google Scholar
Twine, NA, Janitz, K, Wilkins, MR, Janitz, M. Whole transcriptome sequencing reveals gene expression and splicing differences in brain regions affected by Alzheimer’s disease. PLoS One 2011; 6: e16266.CrossRefGoogle ScholarPubMed
Wu, Y, Xu, J, Xu, J, et al. Lower serum levels of miR-29c-3p and miR-19b-3p as biomarkers for Alzheimer’s disease. Tohoku J Exp Med 2017; 242: 129–36.Google Scholar
Qin, J, Xu, Q. Functions and application of exosomes. Acta Pol Pharm 2014; 71: 537–43.Google Scholar
Yuyama, K, Igarashi, Y. Exosomes as carriers of Alzheimer’s amyloid-ß. Front Neurosci 2017; 11;DOI: http://doi.org/10.3389/fnins.2017.00229.Google Scholar
Vella, LJ, Sharples, RA, Nisbet, RM, Cappai, R, Hill, AF. The role of exosomes in the processing of proteins associated with neurodegenerative diseases. Eur Biophys J 2008; 37: 323–32.Google Scholar
Johnstone, RM, Adam, M, Hammond, JR, Orr, L, Turbide, C. Vesicle formation during reticulocyte maturation. Association of plasma membrane activities with released vesicles (exosomes). J Biol Chem 1987; 262: 9412–20.Google Scholar
Malm, T, Loppi, S, Kanninen, KM. Exosomes in Alzheimer’s disease. Neurochem Int 2016; 97: 193–9.Google Scholar
Théry, C, Ostrowski, M, Segura, E. Membrane vesicles as conveyors of immune responses. Nat Rev Immunol 2009; 9: 581.Google Scholar
Joshi, P, Turola, E, Ruiz, A, et al. Microglia convert aggregated amyloid-β into neurotoxic forms through the shedding of microvesicles. Cell Death Differ 2014; 21: 582–93.Google Scholar
Chivet, M, Hemming, F, Pernet-Gallay, K, Fraboulet, S, Sadoul, R. Emerging role of neuronal exosomes in the central nervous system. Front Physiol 2012; 3: 145.Google Scholar
Colombo, E, Borgiani, B, Verderio, C, Furlan, R. Microvesicles: novel biomarkers for neurological disorders. Front Physiol 2012; 3: 63.Google Scholar
Verderio, C, Muzio, L, Turola, E, et al. Myeloid microvesicles are a marker and therapeutic target for neuroinflammation. Ann Neurol 2012; 72: 610–24.Google Scholar
Raposo, G, Stoorvogel, W. Extracellular vesicles: exosomes, microvesicles, and friends. J Cell Biol 2013; 200: 373–83.Google Scholar
Sato, Y, Suzuki, I, Nakamura, T, et al. Identification of a new plasma biomarker of Alzheimer’s disease using metabolomics technology. J Lipid Res 2012; 53: 567–76.CrossRefGoogle ScholarPubMed
Wilcoxen, KM, Uehara, T, Myint, KT, Sato, Y, Oda, Y. Practical metabolomics in drug discovery. Expert Opin Drug Discov 2010; 5: 249–63.Google Scholar
Graham, SF, Chevallier, OP, Elliott, CT, et al. Untargeted metabolomic analysis of human plasma indicates differentially affected polyamine and L-arginine metabolism in mild cognitive impairment subjects converting to Alzheimer’s disease. PLoS One 2015; 10: https://doi.org/10.1371/journal.pone.0119452.Google Scholar
Kaddurah-Daouk, R, Zhu, H, Sharma, S, et al. Alterations in metabolic pathways and networks in Alzheimer’s disease. Transl Psychiatry 2013; 3: e244.Google Scholar
Kori, M, Aydın, B, Unal, S, Arga, KY, Kazan, D. Metabolic biomarkers and neurodegeneration: a pathway enrichment analysis of Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis. OMICS J Integr Biol 2016; 20: 645–61.Google Scholar
Mousavi, M, Jonsson, P, Antti, H, et al. Serum metabolomic biomarkers of dementia. Dement Geriatr Cogn Disord Extra 2014; 4: 252–62.Google Scholar
Toledo, JB, Arnold, M, Kastenmüller, G, et al. Metabolic network failures in Alzheimer’s disease: a biochemical road map. Alzheimers Dement 2017; 13: 965–84.Google Scholar
Trushina, E, Mielke, MM. Recent advances in the application of metabolomics to Alzheimer’s disease. Biochim Biophys Acta 2014; 1842: 1232–9.Google Scholar
Voyle, N, Kim, M, Proitsi, P, et al. Blood metabolite markers of neocortical amyloid-β burden: discovery and enrichment using candidate proteins. Transl Psychiatry 2016; 6: e719.CrossRefGoogle ScholarPubMed
Orešič, M, Hyötyläinen, T, Herukka, S-K, et al. Metabolome in progression to Alzheimer’s disease. Transl Psychiatry 2011; 1: e57.Google Scholar
Cui, Y, Liu, X, Wang, M, et al. Lysophosphatidylcholine and amide as metabolites for detecting Alzheimer disease using ultrahigh-performance liquid chromatography–quadrupole time-of-flight mass spectrometry–based metabonomics. J Neuropathol Exp Neurol 2014; 73: 954–63.Google Scholar
Zhou, M, Haque, RU, Dammer, EB, et al. Targeted mass spectrometry to quantify brain-derived cerebrospinal fluid biomarkers in Alzheimer’s disease. Clin Proteomics 2020; 17: 19.CrossRefGoogle ScholarPubMed
Sapkota, S, Huan, T, Tran, T, et al. Metabolomics analyses of salivary samples discriminate normal aging, mild cognitive impairment, and Alzheimer’s disease groups and produce biomarkers predictive of neurocognitive performance. Alzheimers Dement 2015; 11: P654.Google Scholar
Liang, Q, Liu, H, Zhang, T, et al. Metabolomics-based screening of salivary biomarkers for early diagnosis of Alzheimer’s disease. RSC Adv 2015; 5: 96074–9.Google Scholar

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